Emerging Technologies

How pairing digital twin technology with AI could boost buildings’ emissions reductions

City,  Globe - Navigational Equipment, Computer Network, Technology, Digital Display; digital twin

Digital twins paired with AI can help building owners realise significant cost and operating efficiencies. Image: Getty Images/MarsYu

Philip Panaro
Former CEO & Founder, BCG Platinion North & Latin America
Sarah Parlow
Director of Sustainability, ESGDigital.Ai
François Amman
Chief Executive Officer, Akila
This article is part of: Centre for Urban Transformation
  • Around the world, new regulations are targeting building emissions. From this year, for example, New York City will impose strict caps on more than 40,000 buildings to combat greenhouse gas emissions.
  • Digital twins are virtual building replicas, which enable real-time analysis of climate-related data, predictive maintenance capabilities and cost savings.
  • In the future, digital twins could lead to cognitive twins – intelligent systems capable of predictive analysis, anomaly detection and autonomous operation. This could enhance building efficiency and occupant comfort.

This year, New York City will start enforcing the most ambitious building performance standard legislation in the world. Local Law 97 (LL97), enacted in 2019, caps emissions from buildings over 25,000 square feet and applies to more than 40,000 buildings.

The number of jurisdictions adopting building performance standards in the United States has nearly doubled since 2020. Buildings account for more than 30% of all greenhouse gas emissions in the US. In New York City, a densely populated area, buildings contribute nearly 70% of overall emissions.

As the number of building performance standards grows to combat these emissions, simplifying and automating environmental, social and governance (ESG) reporting, and enabling implementation of energy conservation measures, is becoming more important.

But existing building management systems and ESG reporting tools are limited in scope and operate in siloes. Building management systems are also often proprietary, so they are incompatible with technologies and equipment from different vendors. As a result, ESG reporting is highly resource intensive. Collecting Scope 3 data – emissions information from suppliers and vendors – is also incredibly challenging.

Digital twin technology paired with artificial intelligence (AI) could be a promising solution. This could help building management systems realise significant cost savings and operating efficiency using predictive maintenance, scenario testing, risk management and automated real-time reporting.

Loading...

What is a digital twin?

A digital twin is a virtual replica of a physical object such as a building, including all of its equipment and operating systems. Smart meters and equipment sensors transmit data wirelessly to the digital twin via the cloud, so accurate, fine-grained data can be captured and tracked in real time on a single platform.

Such platforms can collect and analyse vast amounts of data from multiple sources across a campus, portfolio of buildings or supply chain. This means energy, emission, water, waste, occupancy, air quality and transportation data can be tracked, traced and monitored. This high-fidelity, highly synchronized data is then fed into Al systems to support informed and precise decision making. An organization’s Scope 1, 2 and 3 emissions reports can then be generated in real-time to support compliance with benchmarking or reporting frameworks.

Comparison of physical building and a digital twin of a structure.
Digital twin technology can collect and analyse vast amounts of data from multiple sources across a campus, portfolio of buildings or supply chain. Image: Akila

Global home furnishings company IKEA is already using this technology. The big box furniture store has digitalized 37 of its retail stores in East Asia in only nine months to improve energy efficiency and reduce greenhouse gas emissions. It was able to connect 7,000 different data points from various building and energy management, and Internet of Things (loT) systems to a single digital twin.

Ikea’s digital twin represented nearly 42 million square feet of space and 6,000 pieces of heating ventilation and air conditioning (HVAC) equipment from 10 different manufacturers. Energy consumption by the HVAC central supply system was reduced by 30% with annual energy cost savings estimated to be in the millions.

The next step: cognitive digital twins

Over time, buildings with Al-driven digital twin systems will evolve to become “cognitive” by learning from existing processes and patterns. They will detect anomalies, recommend corrective action and predict faults before they occur.

These “intelligent” systems would provide a critical support service to facilities staff and extend equipment life, improving return on investment and operating costs. By monitoring and maintaining room-level heating, cooling and air quality, these systems will also vastly improve building occupants’ health and reduce sick days.

As the level of automation advances, operating efficiency is fine-tuned. This reduces energy use, labour, emissions and operating costs. In the future, cognitive buildings might resemble Tesla’s self-driving cars – essentially running themselves with human oversight and the ability to override.

Cognitive buildings designed to test scenarios in the virtual world using digital twin technology can minimize risk and prevent costly mistakes in the real world. This might include anything from estimating how much battery storage should accompany the installation of solar panels, to modelling how the panels will perform during an extreme weather event, or gauging how electrification will affect your electrical load and energy bills. Complex decision making becomes more accurate, data-driven and collaborative as a result.

For example, retrofits of critical systems such as HVACs are traditionally carried out with little to no visibility prior to the projects, and often without any visibility after the fact. Digital twin technology gives building owners the opportunity to “test before they invest” in retrofits. Not only does it allow them to build a baseline for the cost, energy performance and carbon emissions impacts, it enables them to simulate how those numbers might look post-retrofit – before they even spend a dollar.

Have you read?

Today, only a handful of cities have built viable digital twins, but research shows that over 500 cities will be using digital twins paired with AI by 2025. If businesses are to reach climate goals sooner rather than later, digital twins and Al must play a role in decarbonizing the built environment.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

Sign up for free

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Stay up to date:

Emerging Technologies

Related topics:
Emerging TechnologiesUrban TransformationClimate Action
Share:
The Big Picture
Explore and monitor how Climate Crisis is affecting economies, industries and global issues
A hand holding a looking glass by a lake
Crowdsource Innovation
Get involved with our crowdsourced digital platform to deliver impact at scale
World Economic Forum logo
Global Agenda

The Agenda Weekly

A weekly update of the most important issues driving the global agenda

Subscribe today

You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy.

10 start-ups to watch in the longevity economy

Hope French and Michael Atkinson

November 7, 2024

How AI could help modernize pension and retirement systems

About us

Engage with us

  • Sign in
  • Partner with us
  • Become a member
  • Sign up for our press releases
  • Subscribe to our newsletters
  • Contact us

Quick links

Language editions

Privacy Policy & Terms of Service

Sitemap

© 2024 World Economic Forum